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HLEGF: An Effective Hypernetwork Community Detection Algorithm Based on Local Expansion and Global Fusion

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  • Feng Wang

    (School of Computer, Qinghai Normal University, Xining 810008, China
    The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining 810008, China)

  • Feng Hu

    (School of Computer, Qinghai Normal University, Xining 810008, China
    The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining 810008, China)

  • Rumeng Chen

    (School of Computer, Qinghai Normal University, Xining 810008, China
    The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining 810008, China)

  • Naixue Xiong

    (Department of Computer, Mathematical and Physical Sciences, Sul Ross State University, Alpine, TX 79830, USA)

Abstract

Community structure is crucial for understanding network characteristics, and the local expansion method has performed well in detecting community structures. However, there are two problems with this method. Firstly, it can only add nodes or edges on the basis of existing clusters, and secondly, it can produce a large number of small communities. In this paper, we extend the local expansion method based on ordinary graph to hypergraph, and propose an effective hypernetwork community detection algorithm based on local expansion (LE) and global fusion (GF), which is referred to as HLEGF. The LE process obtains multiple small sub-hypergraphs by deleting and adding hyperedges, while the GF process optimizes the sub-hypergraphs generated by the local expansion process. To solve the first problem, the HLEGF algorithm introduces the concepts of community neighborhood and community boundary to delete some nodes and hyperedges in hypergraphs. To solve the second problem, the HLEGF algorithm establishes correlations between adjacent sub-hypergraphs through global fusion. We evaluated the performance of the HLEGF algorithm in the real hypernetwork and six synthetic random hypernetworks with different probabilities. Because the HLEGF algorithm introduces the concepts of community boundary and neighborhood, and the concept of a series of similarities, the algorithm has superiority. In the real hypernetwork, the HLEGF algorithm is consistent with the classical Spectral algorithm, while in the random hypernetwork, when the probability is not less than 0.95, the NMI value of the HLEGF algorithm is always greater than 0.92, and the RI value is always greater than 0.97. When the probability is 0.95, the HLEGF algorithm achieves a 2.3% improvement in the NMI value, compared to the Spectral algorithm. Finally, we applied the HLEGF algorithm to the drug–target hypernetwork to partition drugs with similar functions into communities.

Suggested Citation

  • Feng Wang & Feng Hu & Rumeng Chen & Naixue Xiong, 2023. "HLEGF: An Effective Hypernetwork Community Detection Algorithm Based on Local Expansion and Global Fusion," Mathematics, MDPI, vol. 11(16), pages 1-17, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3497-:d:1216344
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    References listed on IDEAS

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    1. Liu, Zhong & Huang, Jincai & Cheng, Guangquan, 2016. "Community detection in hypernetwork via Density-Ordered Tree partitionAuthor-Name: Cheng, Qing," Applied Mathematics and Computation, Elsevier, vol. 276(C), pages 384-393.
    2. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    3. Estrada, Ernesto & Rodríguez-Velázquez, Juan A., 2006. "Subgraph centrality and clustering in complex hyper-networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 581-594.
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    1. Youzhi Qu & Kai Fu & Linjing Wang & Yu Zhang & Haiyan Wu & Quanying Liu, 2024. "Hypergraph-Based Multitask Feature Selection with Temporally Constrained Group Sparsity Learning on fMRI," Mathematics, MDPI, vol. 12(11), pages 1-16, June.

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